Figure3A

Load Data

dataset <- read.delim("raw_data/Figure3A.txt", stringsAsFactors = FALSE)

dataset$genotype <-  factor(gsub(" $","",dataset$genotype))
dataset$Experiment <- factor(rep(paste0("exp", 1:(length(dataset$genotype)/length(levels(dataset$genotype)))), each=length(unique(dataset$genotype))))

# genotype+Experiment gives unique ID (UID)
dataset$UID <- factor(paste(dataset$Experiment, dataset$genotype))

# wide format
kable(dataset, row.names = F)

genotype	NT	MMS_0.0003	MMS_0.0006	MMS_0.001	Experiment	UID
WT	2272	1946	1676	1422	exp1	exp1 WT
PARP1 KO	1004	756	615	388	exp1	exp1 PARP1 KO
ALC1 KO	2336	1756	1396	700	exp1	exp1 ALC1 KO
ALC1 KO PARP1 KO	2424	1855	1355	751	exp1	exp1 ALC1 KO PARP1 KO
WT	1224	1110	1051	920	exp2	exp2 WT
PARP1 KO	1150	955	677	422	exp2	exp2 PARP1 KO
ALC1 KO	1760	1540	966	588	exp2	exp2 ALC1 KO
ALC1 KO PARP1 KO	1677	1489	1023	612	exp2	exp2 ALC1 KO PARP1 KO
WT	1608	1553	1295	1121	exp3	exp3 WT
PARP1 KO	988	756	621	328	exp3	exp3 PARP1 KO
ALC1 KO	1968	1568	999	652	exp3	exp3 ALC1 KO
ALC1 KO PARP1 KO	1366	1120	785	433	exp3	exp3 ALC1 KO PARP1 KO

library(reshape2)
# reshape to long format
dataset <- melt(dataset, variable.name = "Treatment", value.name = "Counts")

dataset$genotype <- relevel(dataset$genotype, ref = "WT")
dataset$UID <- relevel(dataset$UID, ref = "exp1 WT")

dataset$MMS <- gsub("NT","0",dataset$Treatment)
dataset$MMS <- gsub(",",".",gsub("MMS_","",dataset$MMS))
dataset$MMS <- (as.numeric(dataset$MMS))




dataset$Offset <- NA
for(uid in levels(dataset$UID)){
        dataset$Offset[dataset$UID == uid] <- mean(dataset$Counts[dataset$UID == uid])
}

dataset$NormCounts <- dataset$Counts / dataset$Offset



dataset$Offset2 <- NA
for(gidx in levels(dataset$genotype)){
        dataset$Offset2[dataset$genotype == gidx] <- mean(dataset$NormCounts[dataset$genotype == gidx & dataset$MMS == 0])
}

dataset$NormCounts2 <- dataset$NormCounts / dataset$Offset2



# long format
kable(dataset, row.names = F)

genotype	Experiment	UID	Treatment	Counts	MMS	Offset	NormCounts	Offset2	NormCounts2
WT	exp1	exp1 WT	NT	2272	0e+00	1829.00	1.2422089	1.177600	1.0548651
PARP1 KO	exp1	exp1 PARP1 KO	NT	1004	0e+00	690.75	1.4534926	1.452235	1.0008657
ALC1 KO	exp1	exp1 ALC1 KO	NT	2336	0e+00	1547.00	1.5100194	1.492670	1.0116231
ALC1 KO PARP1 KO	exp1	exp1 ALC1 KO PARP1 KO	NT	2424	0e+00	1596.25	1.5185591	1.463643	1.0375199
WT	exp2	exp2 WT	NT	1224	0e+00	1076.25	1.1372822	1.177600	0.9657629
PARP1 KO	exp2	exp2 PARP1 KO	NT	1150	0e+00	801.00	1.4357054	1.452235	0.9886175
ALC1 KO	exp2	exp2 ALC1 KO	NT	1760	0e+00	1213.50	1.4503502	1.492670	0.9716483
ALC1 KO PARP1 KO	exp2	exp2 ALC1 KO PARP1 KO	NT	1677	0e+00	1200.25	1.3972089	1.463643	0.9546102
WT	exp3	exp3 WT	NT	1608	0e+00	1394.25	1.1533082	1.177600	0.9793720
PARP1 KO	exp3	exp3 PARP1 KO	NT	988	0e+00	673.25	1.4675084	1.452235	1.0105168
ALC1 KO	exp3	exp3 ALC1 KO	NT	1968	0e+00	1296.75	1.5176403	1.492670	1.0167286
ALC1 KO PARP1 KO	exp3	exp3 ALC1 KO PARP1 KO	NT	1366	0e+00	926.00	1.4751620	1.463643	1.0078698
WT	exp1	exp1 WT	MMS_0.0003	1946	3e-04	1829.00	1.0639694	1.177600	0.9035068
PARP1 KO	exp1	exp1 PARP1 KO	MMS_0.0003	756	3e-04	690.75	1.0944625	1.452235	0.7536399
ALC1 KO	exp1	exp1 ALC1 KO	MMS_0.0003	1756	3e-04	1547.00	1.1351002	1.492670	0.7604495
ALC1 KO PARP1 KO	exp1	exp1 ALC1 KO PARP1 KO	MMS_0.0003	1855	3e-04	1596.25	1.1620987	1.463643	0.7939767
WT	exp2	exp2 WT	MMS_0.0003	1110	3e-04	1076.25	1.0313589	1.177600	0.8758144
PARP1 KO	exp2	exp2 PARP1 KO	MMS_0.0003	955	3e-04	801.00	1.1922597	1.452235	0.8209824
ALC1 KO	exp2	exp2 ALC1 KO	MMS_0.0003	1540	3e-04	1213.50	1.2690564	1.492670	0.8501923
ALC1 KO PARP1 KO	exp2	exp2 ALC1 KO PARP1 KO	MMS_0.0003	1489	3e-04	1200.25	1.2405749	1.463643	0.8475937
WT	exp3	exp3 WT	MMS_0.0003	1553	3e-04	1394.25	1.1138605	1.177600	0.9458736
PARP1 KO	exp3	exp3 PARP1 KO	MMS_0.0003	756	3e-04	673.25	1.1229113	1.452235	0.7732295
ALC1 KO	exp3	exp3 ALC1 KO	MMS_0.0003	1568	3e-04	1296.75	1.2091768	1.492670	0.8100765
ALC1 KO PARP1 KO	exp3	exp3 ALC1 KO PARP1 KO	MMS_0.0003	1120	3e-04	926.00	1.2095032	1.463643	0.8263647
WT	exp1	exp1 WT	MMS_0.0006	1676	6e-04	1829.00	0.9163477	1.177600	0.7781487
PARP1 KO	exp1	exp1 PARP1 KO	MMS_0.0006	615	6e-04	690.75	0.8903366	1.452235	0.6130801
ALC1 KO	exp1	exp1 ALC1 KO	MMS_0.0006	1396	6e-04	1547.00	0.9023917	1.492670	0.6045487
ALC1 KO PARP1 KO	exp1	exp1 ALC1 KO PARP1 KO	MMS_0.0006	1355	6e-04	1596.25	0.8488645	1.463643	0.5799668
WT	exp2	exp2 WT	MMS_0.0006	1051	6e-04	1076.25	0.9765389	1.177600	0.8292621
PARP1 KO	exp2	exp2 PARP1 KO	MMS_0.0006	677	6e-04	801.00	0.8451935	1.452235	0.5819948
ALC1 KO	exp2	exp2 ALC1 KO	MMS_0.0006	966	6e-04	1213.50	0.7960445	1.492670	0.5333024
ALC1 KO PARP1 KO	exp2	exp2 ALC1 KO PARP1 KO	MMS_0.0006	1023	6e-04	1200.25	0.8523224	1.463643	0.5823293
WT	exp3	exp3 WT	MMS_0.0006	1295	6e-04	1394.25	0.9288148	1.177600	0.7887355
PARP1 KO	exp3	exp3 PARP1 KO	MMS_0.0006	621	6e-04	673.25	0.9223914	1.452235	0.6351528
ALC1 KO	exp3	exp3 ALC1 KO	MMS_0.0006	999	6e-04	1296.75	0.7703875	1.492670	0.5161138
ALC1 KO PARP1 KO	exp3	exp3 ALC1 KO PARP1 KO	MMS_0.0006	785	6e-04	926.00	0.8477322	1.463643	0.5791931
WT	exp1	exp1 WT	MMS_0.001	1422	1e-03	1829.00	0.7774740	1.177600	0.6602192
PARP1 KO	exp1	exp1 PARP1 KO	MMS_0.001	388	1e-03	690.75	0.5617083	1.452235	0.3867887
ALC1 KO	exp1	exp1 ALC1 KO	MMS_0.001	700	1e-03	1547.00	0.4524887	1.492670	0.3031405
ALC1 KO PARP1 KO	exp1	exp1 ALC1 KO PARP1 KO	MMS_0.001	751	1e-03	1596.25	0.4704777	1.463643	0.3214428
WT	exp2	exp2 WT	MMS_0.001	920	1e-03	1076.25	0.8548200	1.177600	0.7259003
PARP1 KO	exp2	exp2 PARP1 KO	MMS_0.001	422	1e-03	801.00	0.5268414	1.452235	0.3627796
ALC1 KO	exp2	exp2 ALC1 KO	MMS_0.001	588	1e-03	1213.50	0.4845488	1.492670	0.3246189
ALC1 KO PARP1 KO	exp2	exp2 ALC1 KO PARP1 KO	MMS_0.001	612	1e-03	1200.25	0.5098938	1.463643	0.3483730
WT	exp3	exp3 WT	MMS_0.001	1121	1e-03	1394.25	0.8040165	1.177600	0.6827587
PARP1 KO	exp3	exp3 PARP1 KO	MMS_0.001	328	1e-03	673.25	0.4871890	1.452235	0.3354752
ALC1 KO	exp3	exp3 ALC1 KO	MMS_0.001	652	1e-03	1296.75	0.5027955	1.492670	0.3368430
ALC1 KO PARP1 KO	exp3	exp3 ALC1 KO PARP1 KO	MMS_0.001	433	1e-03	926.00	0.4676026	1.463643	0.3194785

Plot Data

library(ggplot2)

# raw data
ggplot(dataset, aes(x=MMS, y=Counts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_smooth(method=lm, formula = y ~ poly(x, 2), se=FALSE, colour="#666666") +
        geom_point(aes(colour=Treatment, shape=Experiment), size=2) +        
        facet_grid(. ~ genotype) +
        xlab(label = "MMS (%)") +
        scale_shape_manual(values=15:19) +
        scale_color_manual(values=c("#999999","#0072B2","#CC79A7","#009E73"))

# NormCounts Linear
ggplot(dataset[dataset$genotype %in% levels(dataset$genotype),], aes(x=MMS, y=NormCounts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(colour="#333333") +
        geom_smooth(method=lm, formula = y ~ x, se=TRUE, colour="#666666") +
        facet_grid(. ~ genotype) +
        xlab(label = "MMS (%)")

# NormCounts Quadratic
ggplot(dataset[dataset$genotype %in% levels(dataset$genotype),], aes(x=MMS, y=NormCounts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(colour="#333333") +
        geom_smooth(method=lm, formula = y ~ poly(x, 2), se=TRUE, colour="#666666") +
        facet_grid(. ~ genotype) +
        xlab(label = "MMS (%)") 

# NormCounts Cubic
ggplot(dataset[dataset$genotype %in% levels(dataset$genotype),], aes(x=MMS, y=NormCounts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(colour="#333333") +
        geom_smooth(method=lm, formula = y ~ poly(x, 3), se=TRUE, colour="#666666") +
        facet_grid(. ~ genotype) +
        xlab(label = "MMS (%)") 

# NormCounts2 Linear
ggplot(dataset[dataset$genotype %in% levels(dataset$genotype),], aes(x=MMS, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(colour="#333333") +
        geom_smooth(method=lm, formula = y ~ x, se=TRUE, colour="#666666") +
        facet_grid(. ~ genotype) +
        xlab(label = "MMS (%)")

# NormCounts2 Quadratic
ggplot(dataset[dataset$genotype %in% levels(dataset$genotype),], aes(x=MMS, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(colour="#333333") +
        geom_smooth(method=lm, formula = y ~ poly(x, 2), se=TRUE, colour="#666666") +
        facet_grid(. ~ genotype) +
        xlab(label = "MMS (%)") 

# NormCounts2 Cubic
ggplot(dataset[dataset$genotype %in% levels(dataset$genotype),], aes(x=MMS, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(colour="#333333") +
        geom_smooth(method=lm, formula = y ~ poly(x, 3), se=TRUE, colour="#666666") +
        facet_grid(. ~ genotype) +
        xlab(label = "MMS (%)") 

library(Cairo)

cairo_pdf("Figure3A_v1.pdf", width = 10, height = 4, family = "Arial")
datasubset <- dataset[dataset$genotype %in% levels(dataset$genotype),]
datasubset$genotype <- relevel(datasubset$genotype, ref = "WT")

ggplot(datasubset, aes(x=MMS, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank(),
              axis.text.x = element_text(angle = 90, hjust = 1)) +
        geom_point(aes(colour = genotype)) +
        geom_smooth(method=lm, formula = y ~ x, se=TRUE, aes(colour = genotype), fill='#DDDDDD', size=0.5) +
        facet_grid(. ~ genotype) +
        xlab(label = "MMS (%)") +
        ylab(label = "Normalized Counts") +
        scale_x_continuous(labels = function(x) format(x, scientific = TRUE)) +
        scale_color_manual(values=c('#000000','#000080','#808080','#800000'))

dev.off()

## quartz_off_screen 
##                 2

cairo_pdf("Figure3A_v2.pdf", width = 5, height = 4, family = "Arial")
datasubset <- dataset[dataset$genotype %in% levels(dataset$genotype),]
datasubset$genotype <- relevel(datasubset$genotype, ref = "WT")

ggplot(datasubset, aes(x=MMS, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank(),
              axis.text.x = element_text(angle = 90, hjust = 1)) +
        geom_point(aes(colour = genotype)) +
        geom_smooth(method=lm, formula = y ~ x, se=TRUE, aes(colour = genotype), fill='#DDDDDD', size=0.5) +
        #facet_grid(. ~ genotype) +
        xlab(label = "MMS (%)") +
        ylab(label = "Normalized Counts") +
        scale_x_continuous(labels = function(x) format(x, scientific = TRUE)) +
        scale_color_manual(values=c('#000000','#000080','#808080','#800000'))

dev.off()

## quartz_off_screen 
##                 2

Models

library(MASS)
library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)

Linear formula

fit1 <- lm(Counts ~ Experiment + MMS*genotype, data = dataset)
print(summary(fit1))

## 
## Call:
## lm(formula = Counts ~ Experiment + MMS * genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -358.11 -116.30   48.29  106.75  364.56 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     1925.11     100.06  19.239  < 2e-16 ***
## Experimentexp2                  -343.00      69.07  -4.966 1.48e-05 ***
## Experimentexp3                  -343.19      69.07  -4.969 1.46e-05 ***
## MMS                          -554124.81  152429.81  -3.635 0.000819 ***
## genotypeALC1 KO                  319.15     129.79   2.459 0.018599 *  
## genotypeALC1 KO PARP1 KO         134.34     129.79   1.035 0.307184    
## genotypePARP1 KO                -660.18     129.79  -5.087 1.01e-05 ***
## MMS:genotypeALC1 KO          -841902.59  215568.30  -3.906 0.000373 ***
## MMS:genotypeALC1 KO PARP1 KO -687732.12  215568.30  -3.190 0.002848 ** 
## MMS:genotypePARP1 KO         -108036.53  215568.30  -0.501 0.619142    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 195.4 on 38 degrees of freedom
## Multiple R-squared:  0.8914, Adjusted R-squared:  0.8657 
## F-statistic: 34.66 on 9 and 38 DF,  p-value: 1.242e-15

cat("AIC: ", AIC(fit1))

## AIC:  653.3867

simres <- simulateResiduals(fittedModel = fit1)
plot(simres)

fit2 <- lm(NormCounts ~ MMS*genotype, data = dataset)
print(summary(fit2))

## 
## Call:
## lm(formula = NormCounts ~ MMS * genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.10063 -0.02897 -0.00040  0.03175  0.08848 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     1.17580    0.02094  56.162  < 2e-16 ***
## MMS                          -370.11386   34.77268 -10.644 3.10e-13 ***
## genotypeALC1 KO                 0.31434    0.02961  10.617 3.34e-13 ***
## genotypeALC1 KO PARP1 KO        0.29819    0.02961  10.071 1.57e-12 ***
## genotypePARP1 KO                0.25990    0.02961   8.778 7.20e-11 ***
## MMS:genotypeALC1 KO          -661.77215   49.17600 -13.457  < 2e-16 ***
## MMS:genotypeALC1 KO PARP1 KO -627.76829   49.17600 -12.766 1.09e-15 ***
## MMS:genotypePARP1 KO         -547.15757   49.17600 -11.127 8.14e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04456 on 40 degrees of freedom
## Multiple R-squared:  0.9843, Adjusted R-squared:  0.9816 
## F-statistic:   359 on 7 and 40 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit2))

## AIC:  -153.1714

simres <- simulateResiduals(fittedModel = fit2)
plot(simres)

fit3 <- lm(NormCounts2 ~ MMS*genotype, data = dataset)
print(summary(fit3))

## 
## Call:
## lm(formula = NormCounts2 ~ MMS * genotype, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.067414 -0.022126 -0.000339  0.021866  0.059274 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   9.985e-01  1.496e-02  66.727  < 2e-16 ***
## MMS                          -3.143e+02  2.485e+01 -12.646 1.47e-15 ***
## genotypeALC1 KO              -1.661e-04  2.116e-02  -0.008    0.994    
## genotypeALC1 KO PARP1 KO      8.597e-03  2.116e-02   0.406    0.687    
## genotypePARP1 KO             -9.859e-03  2.116e-02  -0.466    0.644    
## MMS:genotypeALC1 KO          -3.770e+02  3.515e+01 -10.726 2.46e-13 ***
## MMS:genotypeALC1 KO PARP1 KO -3.675e+02  3.515e+01 -10.455 5.26e-13 ***
## MMS:genotypePARP1 KO         -3.173e+02  3.515e+01  -9.028 3.38e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03185 on 40 degrees of freedom
## Multiple R-squared:  0.9848, Adjusted R-squared:  0.9821 
## F-statistic: 369.5 on 7 and 40 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit3))

## AIC:  -185.4122

simres <- simulateResiduals(fittedModel = fit3)
plot(simres)

fit4 <- lmer(Counts ~ MMS*genotype + (1|UID), data = dataset)
print(summary(fit4))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ MMS * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 470.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.65153 -0.51545  0.00911  0.43069  2.32751 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 69881    264.4   
##  Residual             11740    108.4   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)                   1.696e+03  1.609e+02  9.088e+00  10.544 2.13e-06
## MMS                          -5.541e+05  8.455e+04  3.200e+01  -6.554 2.20e-07
## genotypeALC1 KO               3.192e+02  2.275e+02  9.088e+00   1.403   0.1939
## genotypeALC1 KO PARP1 KO      1.343e+02  2.275e+02  9.088e+00   0.590   0.5693
## genotypePARP1 KO             -6.602e+02  2.275e+02  9.088e+00  -2.902   0.0174
## MMS:genotypeALC1 KO          -8.419e+05  1.196e+05  3.200e+01  -7.041 5.54e-08
## MMS:genotypeALC1 KO PARP1 KO -6.877e+05  1.196e+05  3.200e+01  -5.752 2.23e-06
## MMS:genotypePARP1 KO         -1.080e+05  1.196e+05  3.200e+01  -0.904   0.3730
##                                 
## (Intercept)                  ***
## MMS                          ***
## genotypeALC1 KO                 
## genotypeALC1 KO PARP1 KO        
## genotypePARP1 KO             *  
## MMS:genotypeALC1 KO          ***
## MMS:genotypeALC1 KO PARP1 KO ***
## MMS:genotypePARP1 KO            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) MMS    gALC1K gALKPK gPARPK MMS:AK MMSKPK
## MMS         -0.250                                          
## gntypALC1KO -0.707  0.177                                   
## gALC1KOPARK -0.707  0.177  0.500                            
## gntyPARP1KO -0.707  0.177  0.500  0.500                     
## MMS:gALC1KO  0.177 -0.707 -0.250 -0.125 -0.125              
## MMS:ALC1KPK  0.177 -0.707 -0.125 -0.250 -0.125  0.500       
## MMS:PARP1KO  0.177 -0.707 -0.125 -0.125 -0.250  0.500  0.500
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling

cat("AIC: ", AIC(fit4))

## AIC:  490.699

simres <- simulateResiduals(fittedModel = fit4)
plot(simres)

Quadratic formula

fit5 <- lm(Counts ~ Experiment + poly(MMS, 2)*genotype, data = dataset)
print(summary(fit5))

## 
## Call:
## lm(formula = Counts ~ Experiment + poly(MMS, 2) * genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -368.03 -127.99   45.33  111.29  361.68 
## 
## Coefficients:
##                                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                             1661.90      72.86  22.810  < 2e-16 ***
## Experimentexp2                          -343.00      72.86  -4.708 4.10e-05 ***
## Experimentexp3                          -343.19      72.86  -4.710 4.07e-05 ***
## poly(MMS, 2)1                          -1420.33     412.16  -3.446 0.001531 ** 
## poly(MMS, 2)2                             65.59     412.16   0.159 0.874509    
## genotypeALC1 KO                          -80.75      84.13  -0.960 0.343926    
## genotypeALC1 KO PARP1 KO                -192.33      84.13  -2.286 0.028601 *  
## genotypePARP1 KO                        -711.50      84.13  -8.457 7.08e-10 ***
## poly(MMS, 2)1:genotypeALC1 KO          -2157.97     582.88  -3.702 0.000753 ***
## poly(MMS, 2)2:genotypeALC1 KO             67.12     582.88   0.115 0.909001    
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO -1762.80     582.88  -3.024 0.004719 ** 
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO   -46.60     582.88  -0.080 0.936745    
## poly(MMS, 2)1:genotypePARP1 KO          -276.92     582.88  -0.475 0.637760    
## poly(MMS, 2)2:genotypePARP1 KO           -12.05     582.88  -0.021 0.983622    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 206.1 on 34 degrees of freedom
## Multiple R-squared:  0.8919, Adjusted R-squared:  0.8505 
## F-statistic: 21.57 on 13 and 34 DF,  p-value: 1.094e-12

cat("AIC: ", AIC(fit5))

## AIC:  661.1783

simres <- simulateResiduals(fittedModel = fit5)
plot(simres)

fit6 <- lm(NormCounts ~ poly(MMS, 2)*genotype, data = dataset)
print(summary(fit6))

## 
## Call:
## lm(formula = NormCounts ~ poly(MMS, 2) * genotype, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.084112 -0.027962 -0.003658  0.033302  0.100678 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                             1.000e+00  1.320e-02  75.760  < 2e-16
## poly(MMS, 2)1                          -9.487e-01  9.145e-02 -10.374 2.31e-12
## poly(MMS, 2)2                           3.251e-02  9.145e-02   0.355    0.724
## genotypeALC1 KO                         2.243e-18  1.867e-02   0.000    1.000
## genotypeALC1 KO PARP1 KO                1.652e-17  1.867e-02   0.000    1.000
## genotypePARP1 KO                        3.606e-17  1.867e-02   0.000    1.000
## poly(MMS, 2)1:genotypeALC1 KO          -1.696e+00  1.293e-01 -13.116 2.80e-15
## poly(MMS, 2)2:genotypeALC1 KO           6.761e-02  1.293e-01   0.523    0.604
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO -1.609e+00  1.293e-01 -12.442 1.34e-14
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO -3.297e-02  1.293e-01  -0.255    0.800
## poly(MMS, 2)1:genotypePARP1 KO         -1.402e+00  1.293e-01 -10.844 6.82e-13
## poly(MMS, 2)2:genotypePARP1 KO          4.241e-02  1.293e-01   0.328    0.745
##                                           
## (Intercept)                            ***
## poly(MMS, 2)1                          ***
## poly(MMS, 2)2                             
## genotypeALC1 KO                           
## genotypeALC1 KO PARP1 KO                  
## genotypePARP1 KO                          
## poly(MMS, 2)1:genotypeALC1 KO          ***
## poly(MMS, 2)2:genotypeALC1 KO             
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO ***
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO    
## poly(MMS, 2)1:genotypePARP1 KO         ***
## poly(MMS, 2)2:genotypePARP1 KO            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04572 on 36 degrees of freedom
## Multiple R-squared:  0.9852, Adjusted R-squared:  0.9806 
## F-statistic: 217.2 on 11 and 36 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit6))

## AIC:  -147.7615

simres <- simulateResiduals(fittedModel = fit6)
plot(simres)

fit7 <- lm(NormCounts2 ~ poly(MMS, 2)*genotype, data = dataset)
print(summary(fit7))

## 
## Call:
## lm(formula = NormCounts2 ~ poly(MMS, 2) * genotype, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.056350 -0.020282 -0.002504  0.023290  0.067449 
## 
## Coefficients:
##                                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)                             0.849185   0.009453  89.834  < 2e-16
## poly(MMS, 2)1                          -0.805602   0.065491 -12.301 1.87e-14
## poly(MMS, 2)2                           0.027603   0.065491   0.421    0.676
## genotypeALC1 KO                        -0.179244   0.013368 -13.408 1.44e-15
## genotypeALC1 KO PARP1 KO               -0.165958   0.013368 -12.414 1.43e-14
## genotypePARP1 KO                       -0.160591   0.013368 -12.013 3.73e-14
## poly(MMS, 2)1:genotypeALC1 KO          -0.966345   0.092618 -10.434 1.97e-12
## poly(MMS, 2)2:genotypeALC1 KO           0.039466   0.092618   0.426    0.673
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO -0.941937   0.092618 -10.170 3.95e-12
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO -0.027920   0.092618  -0.301    0.765
## poly(MMS, 2)1:genotypePARP1 KO         -0.813386   0.092618  -8.782 1.77e-10
## poly(MMS, 2)2:genotypePARP1 KO          0.023983   0.092618   0.259    0.797
##                                           
## (Intercept)                            ***
## poly(MMS, 2)1                          ***
## poly(MMS, 2)2                             
## genotypeALC1 KO                        ***
## genotypeALC1 KO PARP1 KO               ***
## genotypePARP1 KO                       ***
## poly(MMS, 2)1:genotypeALC1 KO          ***
## poly(MMS, 2)2:genotypeALC1 KO             
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO ***
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO    
## poly(MMS, 2)1:genotypePARP1 KO         ***
## poly(MMS, 2)2:genotypePARP1 KO            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03275 on 36 degrees of freedom
## Multiple R-squared:  0.9855, Adjusted R-squared:  0.9811 
## F-statistic: 222.6 on 11 and 36 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit7))

## AIC:  -179.8137

simres <- simulateResiduals(fittedModel = fit7)
plot(simres)

fit8 <- lmer(Counts ~ poly(MMS, 2)*genotype + (1|UID), data = dataset)
print(summary(fit8))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(MMS, 2) * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 467.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.56492 -0.48396 -0.07458  0.48306  2.18606 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 69518    263.7   
##  Residual             13193    114.9   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                        Estimate Std. Error       df t value
## (Intercept)                             1433.17     155.79     8.00   9.199
## poly(MMS, 2)1                          -1420.33     229.72    28.00  -6.183
## poly(MMS, 2)2                             65.59     229.72    28.00   0.285
## genotypeALC1 KO                          -80.75     220.33     8.00  -0.367
## genotypeALC1 KO PARP1 KO                -192.33     220.33     8.00  -0.873
## genotypePARP1 KO                        -711.50     220.33     8.00  -3.229
## poly(MMS, 2)1:genotypeALC1 KO          -2157.97     324.88    28.00  -6.642
## poly(MMS, 2)2:genotypeALC1 KO             67.12     324.88    28.00   0.207
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO -1762.80     324.88    28.00  -5.426
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO   -46.60     324.88    28.00  -0.143
## poly(MMS, 2)1:genotypePARP1 KO          -276.92     324.88    28.00  -0.852
## poly(MMS, 2)2:genotypePARP1 KO           -12.05     324.88    28.00  -0.037
##                                        Pr(>|t|)    
## (Intercept)                            1.58e-05 ***
## poly(MMS, 2)1                          1.12e-06 ***
## poly(MMS, 2)2                            0.7774    
## genotypeALC1 KO                          0.7235    
## genotypeALC1 KO PARP1 KO                 0.4081    
## genotypePARP1 KO                         0.0121 *  
## poly(MMS, 2)1:genotypeALC1 KO          3.31e-07 ***
## poly(MMS, 2)2:genotypeALC1 KO            0.8378    
## poly(MMS, 2)1:genotypeALC1 KO PARP1 KO 8.67e-06 ***
## poly(MMS, 2)2:genotypeALC1 KO PARP1 KO   0.8870    
## poly(MMS, 2)1:genotypePARP1 KO           0.4012    
## poly(MMS, 2)2:genotypePARP1 KO           0.9707    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##              (Intr) p(MMS,2)1 p(MMS,2)2 gALC1K gALKPK gPARPK p(MMS,2)1:AK
## ply(MMS,2)1   0.000                                                      
## ply(MMS,2)2   0.000  0.000                                               
## gntypALC1KO  -0.707  0.000     0.000                                     
## gALC1KOPARK  -0.707  0.000     0.000     0.500                           
## gntyPARP1KO  -0.707  0.000     0.000     0.500  0.500                    
## p(MMS,2)1:AK  0.000 -0.707     0.000     0.000  0.000  0.000             
## p(MMS,2)2:AK  0.000  0.000    -0.707     0.000  0.000  0.000  0.000      
## p(MMS,2)1KPK  0.000 -0.707     0.000     0.000  0.000  0.000  0.500      
## p(MMS,2)2KPK  0.000  0.000    -0.707     0.000  0.000  0.000  0.000      
## p(MMS,2)1:PK  0.000 -0.707     0.000     0.000  0.000  0.000  0.500      
## p(MMS,2)2:PK  0.000  0.000    -0.707     0.000  0.000  0.000  0.000      
##              p(MMS,2)2:AK p(MMS,2)1KPK p(MMS,2)2KPK p(MMS,2)1:PK
## ply(MMS,2)1                                                     
## ply(MMS,2)2                                                     
## gntypALC1KO                                                     
## gALC1KOPARK                                                     
## gntyPARP1KO                                                     
## p(MMS,2)1:AK                                                    
## p(MMS,2)2:AK                                                    
## p(MMS,2)1KPK  0.000                                             
## p(MMS,2)2KPK  0.500        0.000                                
## p(MMS,2)1:PK  0.000        0.500        0.000                   
## p(MMS,2)2:PK  0.500        0.000        0.500        0.000

cat("AIC: ", AIC(fit8))

## AIC:  495.3176

simres <- simulateResiduals(fittedModel = fit8)
plot(simres)

Cubic formula

fit9 <- lm(Counts ~ Experiment + poly(MMS, 3)*genotype, data = dataset)
print(summary(fit9))

## 
## Call:
## lm(formula = Counts ~ Experiment + poly(MMS, 3) * genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -363.06 -126.81   51.63  108.77  372.94 
## 
## Coefficients:
##                                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                             1661.90      77.10  21.555  < 2e-16 ***
## Experimentexp2                          -343.00      77.10  -4.449 0.000110 ***
## Experimentexp3                          -343.19      77.10  -4.451 0.000109 ***
## poly(MMS, 3)1                          -1420.33     436.14  -3.257 0.002798 ** 
## poly(MMS, 3)2                             65.59     436.14   0.150 0.881472    
## poly(MMS, 3)3                             68.44     436.14   0.157 0.876361    
## genotypeALC1 KO                          -80.75      89.03  -0.907 0.371616    
## genotypeALC1 KO PARP1 KO                -192.33      89.03  -2.160 0.038854 *  
## genotypePARP1 KO                        -711.50      89.03  -7.992  6.4e-09 ***
## poly(MMS, 3)1:genotypeALC1 KO          -2157.97     616.79  -3.499 0.001482 ** 
## poly(MMS, 3)2:genotypeALC1 KO             67.12     616.79   0.109 0.914069    
## poly(MMS, 3)3:genotypeALC1 KO            128.29     616.79   0.208 0.836636    
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO -1762.80     616.79  -2.858 0.007677 ** 
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO   -46.60     616.79  -0.076 0.940275    
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO    86.66     616.79   0.141 0.889202    
## poly(MMS, 3)1:genotypePARP1 KO          -276.92     616.79  -0.449 0.656682    
## poly(MMS, 3)2:genotypePARP1 KO           -12.05     616.79  -0.020 0.984537    
## poly(MMS, 3)3:genotypePARP1 KO          -110.34     616.79  -0.179 0.859227    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 218.1 on 30 degrees of freedom
## Multiple R-squared:  0.8932, Adjusted R-squared:  0.8326 
## F-statistic: 14.75 on 17 and 30 DF,  p-value: 3.574e-10

cat("AIC: ", AIC(fit9))

## AIC:  668.5997

simres <- simulateResiduals(fittedModel = fit9)
plot(simres)

fit10 <- lm(NormCounts ~ poly(MMS, 3)*genotype, data = dataset)
print(summary(fit10))

## 
## Call:
## lm(formula = NormCounts ~ poly(MMS, 3) * genotype, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.069344 -0.027036  0.000241  0.025537  0.079450 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                             1.000e+00  1.260e-02  79.381  < 2e-16
## poly(MMS, 3)1                          -9.487e-01  8.728e-02 -10.870 2.84e-12
## poly(MMS, 3)2                           3.251e-02  8.728e-02   0.372    0.712
## poly(MMS, 3)3                           4.303e-02  8.728e-02   0.493    0.625
## genotypeALC1 KO                         1.275e-16  1.782e-02   0.000    1.000
## genotypeALC1 KO PARP1 KO                1.334e-16  1.782e-02   0.000    1.000
## genotypePARP1 KO                       -8.574e-17  1.782e-02   0.000    1.000
## poly(MMS, 3)1:genotypeALC1 KO          -1.696e+00  1.234e-01 -13.743 5.68e-15
## poly(MMS, 3)2:genotypeALC1 KO           6.761e-02  1.234e-01   0.548    0.588
## poly(MMS, 3)3:genotypeALC1 KO           1.309e-01  1.234e-01   1.061    0.297
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO -1.609e+00  1.234e-01 -13.037 2.40e-14
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO -3.297e-02  1.234e-01  -0.267    0.791
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO  9.865e-02  1.234e-01   0.799    0.430
## poly(MMS, 3)1:genotypePARP1 KO         -1.402e+00  1.234e-01 -11.363 9.11e-13
## poly(MMS, 3)2:genotypePARP1 KO          4.241e-02  1.234e-01   0.344    0.733
## poly(MMS, 3)3:genotypePARP1 KO         -1.146e-01  1.234e-01  -0.929    0.360
##                                           
## (Intercept)                            ***
## poly(MMS, 3)1                          ***
## poly(MMS, 3)2                             
## poly(MMS, 3)3                             
## genotypeALC1 KO                           
## genotypeALC1 KO PARP1 KO                  
## genotypePARP1 KO                          
## poly(MMS, 3)1:genotypeALC1 KO          ***
## poly(MMS, 3)2:genotypeALC1 KO             
## poly(MMS, 3)3:genotypeALC1 KO             
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO ***
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO    
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO    
## poly(MMS, 3)1:genotypePARP1 KO         ***
## poly(MMS, 3)2:genotypePARP1 KO            
## poly(MMS, 3)3:genotypePARP1 KO            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.04364 on 32 degrees of freedom
## Multiple R-squared:  0.988,  Adjusted R-squared:  0.9823 
## F-statistic: 175.4 on 15 and 32 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit10))

## AIC:  -149.8982

simres <- simulateResiduals(fittedModel = fit10)
plot(simres)

fit11 <- lm(NormCounts2 ~ poly(MMS, 3)*genotype, data = dataset)
print(summary(fit11))

## 
## Call:
## lm(formula = NormCounts2 ~ poly(MMS, 3) * genotype, data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.046457 -0.020582  0.000168  0.017199  0.054865 
## 
## Coefficients:
##                                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)                             0.849185   0.009118  93.137  < 2e-16
## poly(MMS, 3)1                          -0.805602   0.063168 -12.753 4.33e-14
## poly(MMS, 3)2                           0.027603   0.063168   0.437    0.665
## poly(MMS, 3)3                           0.036542   0.063168   0.578    0.567
## genotypeALC1 KO                        -0.179244   0.012894 -13.901 4.14e-15
## genotypeALC1 KO PARP1 KO               -0.165958   0.012894 -12.871 3.39e-14
## genotypePARP1 KO                       -0.160591   0.012894 -12.455 8.18e-14
## poly(MMS, 3)1:genotypeALC1 KO          -0.966345   0.089333 -10.817 3.20e-12
## poly(MMS, 3)2:genotypeALC1 KO           0.039466   0.089333   0.442    0.662
## poly(MMS, 3)3:genotypeALC1 KO           0.080002   0.089333   0.896    0.377
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO -0.941937   0.089333 -10.544 6.11e-12
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO -0.027920   0.089333  -0.313    0.757
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO  0.060256   0.089333   0.675    0.505
## poly(MMS, 3)1:genotypePARP1 KO         -0.813386   0.089333  -9.105 2.13e-10
## poly(MMS, 3)2:genotypePARP1 KO          0.023983   0.089333   0.268    0.790
## poly(MMS, 3)3:genotypePARP1 KO         -0.085854   0.089333  -0.961    0.344
##                                           
## (Intercept)                            ***
## poly(MMS, 3)1                          ***
## poly(MMS, 3)2                             
## poly(MMS, 3)3                             
## genotypeALC1 KO                        ***
## genotypeALC1 KO PARP1 KO               ***
## genotypePARP1 KO                       ***
## poly(MMS, 3)1:genotypeALC1 KO          ***
## poly(MMS, 3)2:genotypeALC1 KO             
## poly(MMS, 3)3:genotypeALC1 KO             
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO ***
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO    
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO    
## poly(MMS, 3)1:genotypePARP1 KO         ***
## poly(MMS, 3)2:genotypePARP1 KO            
## poly(MMS, 3)3:genotypePARP1 KO            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03158 on 32 degrees of freedom
## Multiple R-squared:  0.988,  Adjusted R-squared:  0.9824 
## F-statistic: 175.9 on 15 and 32 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit11))

## AIC:  -180.934

simres <- simulateResiduals(fittedModel = fit11)
plot(simres)

fit12 <- lmer(Counts ~ poly(MMS, 3)*genotype + (1|UID), data = dataset)
print(summary(fit12))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(MMS, 3) * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 415
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.52884 -0.35459 -0.00389  0.40950  2.18082 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 69148    263.0   
##  Residual             14671    121.1   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                        Estimate Std. Error       df t value
## (Intercept)                             1433.17     155.79     8.00   9.199
## poly(MMS, 3)1                          -1420.33     242.25    24.00  -5.863
## poly(MMS, 3)2                             65.59     242.25    24.00   0.271
## poly(MMS, 3)3                             68.44     242.25    24.00   0.283
## genotypeALC1 KO                          -80.75     220.33     8.00  -0.367
## genotypeALC1 KO PARP1 KO                -192.33     220.33     8.00  -0.873
## genotypePARP1 KO                        -711.50     220.33     8.00  -3.229
## poly(MMS, 3)1:genotypeALC1 KO          -2157.97     342.59    24.00  -6.299
## poly(MMS, 3)2:genotypeALC1 KO             67.12     342.59    24.00   0.196
## poly(MMS, 3)3:genotypeALC1 KO            128.29     342.59    24.00   0.374
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO -1762.80     342.59    24.00  -5.145
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO   -46.60     342.59    24.00  -0.136
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO    86.66     342.59    24.00   0.253
## poly(MMS, 3)1:genotypePARP1 KO          -276.92     342.59    24.00  -0.808
## poly(MMS, 3)2:genotypePARP1 KO           -12.05     342.59    24.00  -0.035
## poly(MMS, 3)3:genotypePARP1 KO          -110.34     342.59    24.00  -0.322
##                                        Pr(>|t|)    
## (Intercept)                            1.58e-05 ***
## poly(MMS, 3)1                          4.78e-06 ***
## poly(MMS, 3)2                            0.7889    
## poly(MMS, 3)3                            0.7800    
## genotypeALC1 KO                          0.7235    
## genotypeALC1 KO PARP1 KO                 0.4081    
## genotypePARP1 KO                         0.0121 *  
## poly(MMS, 3)1:genotypeALC1 KO          1.64e-06 ***
## poly(MMS, 3)2:genotypeALC1 KO            0.8463    
## poly(MMS, 3)3:genotypeALC1 KO            0.7113    
## poly(MMS, 3)1:genotypeALC1 KO PARP1 KO 2.88e-05 ***
## poly(MMS, 3)2:genotypeALC1 KO PARP1 KO   0.8929    
## poly(MMS, 3)3:genotypeALC1 KO PARP1 KO   0.8025    
## poly(MMS, 3)1:genotypePARP1 KO           0.4269    
## poly(MMS, 3)2:genotypePARP1 KO           0.9722    
## poly(MMS, 3)3:genotypePARP1 KO           0.7502    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

cat("AIC: ", AIC(fit12))

## AIC:  451.0196

simres <- simulateResiduals(fittedModel = fit12)
plot(simres)

Compare Results

ICtab(fit1,fit2,fit3,fit4,
      fit5,fit6,fit7,fit8,
      fit9,fit10,fit11,fit12,
      base=T)

##       AIC    dAIC   df
## fit3  -185.4    0.0 9 
## fit11 -180.9    4.5 17
## fit7  -179.8    5.6 13
## fit2  -153.2   32.2 9 
## fit10 -149.9   35.5 17
## fit6  -147.8   37.7 13
## fit12  451.0  636.4 18
## fit4   490.7  676.1 10
## fit8   495.3  680.7 14
## fit1   653.4  838.8 11
## fit5   661.2  846.6 15
## fit9   668.6  854.0 19

Final Result

fit <- fit3

output <- coef(summary(fit))
output <- output[grep("MMS", rownames(output)),]

rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )



# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
MMS in WT	-314.2951	24.85330	-12.646013	0
MMS: WT vs. ALC1 KO	-377.0071	35.14787	-10.726313	0
MMS: WT vs. ALC1 KO PARP1 KO	-367.4845	35.14787	-10.455384	0
MMS: WT vs. PARP1 KO	-317.3321	35.14787	-9.028487	0

write.table(output, file = "Figure3A_Stats_Ref_WT.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

# re-fit with ALC1KO reference
dataset$genotype <- relevel(dataset$genotype, ref = "ALC1 KO")
        
fit <- lm(NormCounts2 ~ MMS*genotype, data = dataset)


output <- coef(summary(fit))
output <- output[grep("MMS", rownames(output)),]

rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )



# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
MMS in ALC1 KO	-691.302188	24.85330	-27.8153108	0.0000000
MMS: ALC1 KO vs. WT	377.007067	35.14787	10.7263133	0.0000000
MMS: ALC1 KO vs. ALC1 KO PARP1 KO	9.522602	35.14787	0.2709297	0.7878383
MMS: ALC1 KO vs. PARP1 KO	59.674968	35.14787	1.6978260	0.0973087

write.table(output, file = "Figure3A_Stats_Ref_ALC1.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

Anova

fit3a <- lm(NormCounts2 ~ MMS*genotype, data = dataset)
fit3b <- lm(NormCounts2 ~ MMS+genotype, data = dataset)

# anova table
anova(fit3a, fit3b)

## Analysis of Variance Table
## 
## Model 1: NormCounts2 ~ MMS * genotype
## Model 2: NormCounts2 ~ MMS + genotype
##   Res.Df      RSS Df Sum of Sq      F    Pr(>F)    
## 1     40 0.040582                                  
## 2     43 0.198281 -3   -0.1577 51.812 7.673e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1